Dodge-Jam: Anti-Jamming Technique for Low-Power and Lossy Wireless Networks

Heo, Jeongyoon; Kim, Jungjun; Bahk, Saewoong; Paek, Jeongyeup

doi:10.1109/sahcn.2017.7964926

Cited by 18 publications

(6 citation statements)

References 19 publications

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“…Due to limited powered devices, wireless based IoT systems are more suffered by the jamming attack. Several anti-jamming algorithms has been proposed [71,[73][74][75][76][77][78][79]. In our paper, we briefly discuss the anti-jamming technique implemented using RL.…”

Section: ) Jamming Attack In Iotmentioning

confidence: 99%

Reinforcement Learning for IoT Security: A Comprehensive Survey

Uprety

Rawat

2021

IEEE Internet Things J.

124

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The number of connected smart devices has been increasing exponentially for different Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT systems which has many attack vectors, security flaws and vulnerabilities. Securing billions of connected devices in IoT is a must task to realize the full potential of IoT applications. Recently, researchers have proposed many security solutions for IoT. Machine learning has been proposed as one of the emerging solutions for IoT security and Reinforcement learning is gaining more popularity for securing IoT systems. Reinforcement learning, unlike other machine learning techniques, can learn the environment by having minimum information about the parameters to be learned. It solves the optimization problem by interacting with the environment adapting the parameters on the fly. In this paper, we present an comprehensive survey of different types of cyber-attacks against different IoT systems and then we present reinforcement learning and deep reinforcement learning based security solutions to combat those different types of attacks in different IoT systems. Furthermore, we present the Reinforcement learning for securing CPS systems (i.e., IoT with feedback and control) such as smart grid and smart transportation system. The recent important attacks and countermeasures using reinforcement learning in IoT are also summarized in the form of tables. With this paper, readers can have a more thorough understanding of IoT security attacks and countermeasures using Reinforcement Learning, as well as research trends in this area.

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Section: ) Jamming Attack In Iotmentioning

confidence: 99%

Reinforcement Learning for IoT Security: A Comprehensive Survey

Uprety

Rawat

2021

IEEE Internet Things J.

124

View full text Add to dashboard Cite

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“…From the outcomes, it was recognised that the PSR and PDR were difficult to decide about jamming and its types [17]. • Average number of required transmissions per packet (ATX): Heo et al [18] define the ATX metric as a total number of transmissions divided by the number of successfully received unique packets. • Number of hops for received packets: A Hop count referes to the number of routers that a packet goes through from its source to its destination [19].…”

Section: A Jamming Attacksmentioning

confidence: 99%

Fuzzy-Logic Based IDS for Detecting Jamming Attacks in Wireless Mesh IoT Networks

Savva¹,

Ioannou²,

Vassiliou³

2022

Preprint

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The investigation in this paper targets the design and the evaluation of jamming intrusion detection based on Fuzzy Logic in wireless mesh IoT Networks in a distributed manner. Our approach uses information collected at local nodes and from the sink as input to the fuzzy logic controller. In order to find the best set of inputs, distributed or centralized, we made a comparison between five different combinations of parameters. The investigation uses the values of the ETX, Retransmissions, Packets Drop per terminal (PDPT) and Packet Delivery Ratio (PDR) as inputs to a fuzzy inference system to get Jamming Index (JI) as the system's output. The proposed method was evaluated based on the following metrics: Accuracy, Precision, Specificity, False positive rate (FPR), Recall, False negative rate (FNR) and ROC curve. In order to evaluate this approach, we implement experiments in various scenarios using the Contiki OS and the Cooja simulator tool.

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“…[143] Introduced randomized differential DSSS (RD-DSSS) scheme. [144], [145] Proposed an anti-jamming scheme (Dodge-Jam) based on channel hopping and frame segmentation.…”

Section: Ref Descriptionmentioning

confidence: 99%

“…RD-DSSS decreases the probability of being jammed using the correlation of unpredictable spreading codes. In [145] and [144], a scheme called Dodge-Jam was proposed to defend IEEE 802.15.4 ACK frame transmission against reactive jamming attacks. Dodge-Jam relies on two main techniques: channel hopping and frame segmentation.…”

Section: Anti-jamming Techniquesmentioning

confidence: 99%

Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey

Pirayesh¹,

Zeng²

2021

Preprint

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Wireless networks are a key component of the telecommunications infrastructure in our society, and wireless services become increasingly important as the applications of wireless devices have penetrated every aspect of our lives. Although wireless technologies have significantly advanced in the past decades, most wireless networks are still vulnerable to radio jamming attacks due to the openness nature of wireless channels, and the progress in the design of jamming-resistant wireless networking systems remains limited. This stagnation can be attributed to the lack of practical physical-layer wireless technologies that can efficiently decode data packets in the presence of jamming attacks. This article surveys existing jamming attacks and anti-jamming strategies in wireless local area networks (WLANs), cellular networks, cognitive radio networks (CRNs), ZigBee networks, Bluetooth networks, vehicular networks, LoRa networks, RFID networks, and GPS system, with the objective of offering a comprehensive knowledge landscape of existing jamming/anti-jamming strategies and stimulating more research efforts to secure wireless networks against jamming attacks. Different from prior survey papers, this article conducts a comprehensive, in-depth review on jamming and anti-jamming strategies, casting insights on the design of jamming-resilient wireless networking systems. An outlook on promising antijamming techniques is offered at the end of this article to delineate important research directions.

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Dodge-Jam: Anti-Jamming Technique for Low-Power and Lossy Wireless Networks

Cited by 18 publications

References 19 publications

Reinforcement Learning for IoT Security: A Comprehensive Survey

Reinforcement Learning for IoT Security: A Comprehensive Survey

Fuzzy-Logic Based IDS for Detecting Jamming Attacks in Wireless Mesh IoT Networks

Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey

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